基于多尺度密集连接网络的电容层析成像图像重建OA北大核心CSTPCD

In order to solve the nonlinear ill-posed inverse problem in electrical capacitance tomography(ECT),a multiscale dense connection network(multi-scale densely connected network,MD-Net)model is proposed.The model consists of a multiscale feature fusion module and a densely connected block to further improve the reconstruction accuracy of images by fusing multiscale features.A flow-type data set is constructed by the MATLAB simulation experiment platform,and the learning and training of the training set are completed by using the nonlinear mapping ability of the densely connected network.The training effect is evaluated by using the test set.Static experiments are conducted on this basis.The simulation and static experiments results show that the method has the highest reconstruction accuracy,good noise immunity,and generalization ability compared with LBP,Landweber iterative algorithm,and other deep learning methods.